臺灣人文及社會科學引文索引資料庫系統

:::

詳目顯示

回上一頁
第 1 筆 / 總合 1 筆   第一頁 上一頁 下一頁 最後一頁
/1頁
題名：多目標塑膠射出成形之製程參數最佳化系統
作者：傅公良
作者(外文)：FU,Gong-Loung
校院名稱：中華大學
系所名稱：科技管理學系(所)
指導教授：陳 文 欽
王 珉 玟
學位類別：博士
出版日期：2010
主題關鍵詞：反應曲面法；田口方法；類神經網路法；基因演算法；模擬退火法；粒子族群演算法；response surface methodology；Taguchi method；back-propagation neural network；genetic algorithm；simulated annealing；particle swarm optimization
原始連結：連回原系統網址new window
相關次數：
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:19
塑膠射出成形技術已廣泛地應用於高科技產業，由於塑膠製品多樣化及輕薄短小的趨勢，且希望能有更精準的控制，更快速的反應，以生產高精度、高利潤之產品，而如何有效掌握產品品質，是攸關量產良率之主因。射出成形系統整體製程複雜，射出成形機之成形參數及其系統控制模式大多為非線性而難以求得最適解，且隨著未知參數的增加，其複雜度將是以幾何級數呈現大幅度變化。
在以往產品品質之衡量通常以單一品質特性目標做衡量，就算是多品質特性，各品質指標間衡量往往是互相獨立的，由於產品之品質通常不只衡量一個品質特性，且各品質特性間多具有關聯性，因此要將目前製程高度自動化的產品品質提升，其中設備的使用率、機台精密度、環境與設備的操控者之間的變數等，對產品品質都具有相當大的影響。但是在現有的射出成形的控制架構中，不論是機台的特性差異或外在雜訊的影響，對於受控系統均有不確定的干擾，因此在實際環境的應用上，CAE 軟體所分析得到的控制參數並不完全適合直接應用於機台上，因此亟需設計ㄧ具有智慧成形之參數最佳化系統。
本研究提出建構多目標塑膠射出成形之三階段製程參數最佳化系統，第一、二階段以CAE 軟體執行DOE 因子篩選模擬實驗及結合RSM-GA 參數優化所得之製程參數最佳設定，提供第三階段實際射出製程參數控制之科學依據。本文CAE 軟體是利用Moldex3D 模流分析軟體，搭配Rhinoceros 4.0 為建模平台來製作網格(mesh)，產生解析度足夠的網格；並由電腦模擬分析結果達到開模前預測產品可能形成之缺陷，減少產品研發的週期(cycle time)，增加可靠度，可有效提升開模效率及降低試誤成本。第三階段進行了L25 田口實驗將實際射出製程參數優化- 透過實驗計算出訊號雜音比(S/N Ratio)及靈敏度並應用ANOVA 分析，找出實驗顯著因子之最初製程參數組合，另將實驗數據資料經由倒傳遞神經網路(Back-Propagation Neural Network)訓練與測試，建構S/N 比預測器，輔以S/N 比預測器搭配模擬退火法(Simulated Annealing,SA)，進行製程參數S/N 比最佳化，使製程變異數最小達至穩定狀態，最後針對品質特性進行ANOVA 分析及倒傳遞神經網路訓練與測試建構品質預測器，以品質預測器搭配粒子群最佳化演算法(Particle Swarm Optimization, PSO)，進行製程參數多品質最佳化，更進一步逼近產品所要求之多品質目標值規範，使製程不僅穩定，也同時符合產品多品質規範。因此本研究主要目的是運用CAE 模擬與智慧成形參數之最佳化方法，針對射出成形產品製程參數作整合性研究，進而達到優化模具及有效降低成本的目標。
The plastic injection molding (PIM) has been widely researched and routinely applied in many high-tech industries; the accuracy and precision of this technology are being severely scrutinized in the wake of the evolutionary trend of making products diverse and creative. In this regard, how to effectively grasp the best and suitable quality of products is always the crucial issue associated with the throughput and the yield. By virtue of the underlying plastic injection molding system causing the process parameter settings in injection molding machines to be complicated, the nonlinear control model for the injection molding system is hard to be obtained with exponentially changed complexity while more unknown parameters are added.
In the past, PIM product quality was usually measured by one single quality
characteristic or by multiple quality characteristics with independent parameters one another. With the increasing complexity of product, this dissertation proposes a three-stage integrated optimization system to generate the optimal process parameter settings of multiple-quality characteristics. In the first stage, the significant PIM process parameters can be determined by DOE screening experiments. In the second stage, the optimal process parameter settings are obtained via computer aided engineering (CAE) simulation integrated with response surface methodology (RSM) and genetic algorithm (GA) , which are taken as practically initial settings of process-related parameters. As for the previous two stages, the mold-flow analysis software (i.e., Moldex3D and Rhinoceros 4.0) is used as a working platform to develop mesh with a sufficient resolution, and predict possible defects observed on one product by way of a computer’s simulated results for reduced cycle time, increased reliability, improved efficiency in die sinking, and decreased cost from CAE simulation instead of trial-and-error process prior to die sinking.
In the final stage, Taguchi method and back-propagation neural network (BPNN) are utilized for developing a signal-to-noise (S/N) ratio predictor and executing analysis of variance (ANOVA) to analyze the factors’ significance effects of initial process-related parameter settings, then the BPNN S/N ratio predictor conducting with simulated annealing (SA) to optimize the process-related parameter settings for S/N ratios characteristic and minimize/stabilize variations of one process; moreover, the BPNN quality predictor also works in with particle swarm optimization (PSO) for multi-quality/ multi-objective characteristics (i.e., length and warpage) optimization of process-related process parameters.
In addition, the above-mentioned CAE simulations and intelligent integrated process parameter optimization system are employed to search the optimal PIM process parameters,and can further obtain the optimal die design and effectively reduce the costs.
Anjum, M. F., Tasadduq, I., & Al-Sultan, K. (1997). Response surface methodology: A
neural network approach. European Journal of Operational Research, 101, 65-73.
Che, Z. H. (2010). PSO-based back-propagation artificial neural network for product and
mold cost estimation of plastic injection molding. Computers and Industrial
Engineering, 58, 625-637.
Chen, W. C., Fu, G. L., Tai, P. H., & Deng, W. J. (2009). Process parameter optimization for
MIMO plastic injection molding via soft computing. Expert Systems with Applications,
36, 1114-1122.
Chen, W. C., Tai, P. H., Wang, M. W., Deng, W. J., & Chen, C. T. (2008). A neural
network-based approach for a dynamic quality predictor in plastic injection molding
process. Expert System with Applications, 35(3), 843-849.
Chen, W. C., Wang, M. W., Chen, C. T., & Fu, G. L. (2009). An integrated parameter
optimization system for MISO plastic injection molding. International Journal
Advanced Manufacturing Technology, 44, 501-511.
Cheng, C. S. (1995). A multi-layer neural network model for detecting changes in the
process mean. Computers & Industrial Engineering, 28(1), 51-61.
Chiang, K. T., & Chang, F. P. (2006). Application of grey-fuzzy logic on the optimal process
design of an injection-molded part with a thin shell feature. International
Communications in Heat and Mass Transfer, 33, 94-101.
Chiu, C. C., Su, C. T., Yang, G. H., Huang, J. S., Chen, S. C., & Chen, N. T. (1997). Selection
of optimal parameters in gas assisted injection molding using a neural network model
and the Taguchi method. International Journal of Quality Science, 2, 106-120.
Eberhart, R. C., & Kennedy, J. (1995). A new optimizer using particle swarm theory.
Proceedings of the Sixth International Symposium on Micromachine and Human
Science, Nagoya, Japan, pp.39-43.
Geethanjali, M., Slochanal, S. M. R., & Bhavani, R. (2008). PSO trained ANN-based
differential protection scheme for power transformers. Neurocomputing, 71,
(4/6),904-918.
Ghani, J. A., Choudhury, I. A., & Hassan, H. H. (2003). Application of Taguchi method in
the optimization of end milling parameters. International Journal of Materials
Processing Technology, 145, 84-92.
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning.
New York, NY: Addison-Wesley.
Holland, J. H. (1975). Adaptation in Natural and Artificial Systems, Ann Arbor, The University of Michigan Press.
Hou, T. H., Su, C. H., & Liu, W. L. (2007). Parameters optimization of a nano-particle wet
milling process using the Taguchi method, response surface method and genetic
algorithm, Powder Technology, 173(3), 153-162.
Hsu, C. M. (2004a) An integrated approach to enhance the optical performance of couplers
based on neural networks, desirability functions and tabu search. International journal
of production economics, 92, 241-254.
Hsu, C. M. (2004b). Improving the electroforming process in optical recordable media
manufacturing via integrated procedure. Engineering Optimization, 36(6), 659-675.
Hsu, C. M., Su, C. T., & Liao, D. (2003). A novel approach for optimizing the optical
performance of the broadband tap coupler. International Journal of Systems Science,
34(3), 215-226.
Huang, C. C., & Tang, T. T. (2006). Parameter optimization in melt spinning by neural
networks and genetic algorithms. The International Journal of Advanced
Manufacturing Technology, 27(11), 1113-1118.
Huang, M. C., & Tai, C. C. (2001). The effective factors in the warpage problem of an
injection-molded part with a thin shell feature. Journal of Materials Processing
Technology, 110, 1-9.
Hush, D. R., & Horne, B. G. (1993). Progress in supervised neural networks. IEEE Signal
Processing Magazine, January, pp.8-15.
Kirkpatrick, S., Gellatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing.
Science, 220, 671-680.
Kurtaran, H., & Erzurumlu, T. (2006). Efficient warpage optimization of thin shell plastic
parts using response surface methodology and genetic algorithm. The International
Journal of Advanced Manufacturing Technology, 27(5/6), 468-472.
Kurtaran, H., Ozcelik, B., & Erzurumlu, T. (2005). Warpage optimization of a bus ceiling
lamp base using neural network model and genetic algorithm. Journal of Materials
Processing Technology, 169(2), 314-319.
Lam, Y. C., Deng, Y. M., & Au, C. K. (2006). A GA/gradient hybrid approach for injection
moulding conditions optimization. Engineering with Computers, 21(3), 193-202.
Lam, Y. C., Zhai, L. Y., Tai, K., & Fok, S. C. (2004). An evolutionary approach for cooling
system optimization in plastic injection moulding. International Journal of Production
Research, 42(10), 2047-2061.
Lau, H. C. W., Ning, A., Pun, K. F., & Chin, K. S. (2001). Neural networks for the
dimensional control of molded parts based on a reverse process model. International
Journal of Materials Processing Technology, 117, 89-96.
Li, E., Jia, L., & Yu, J. (2002). A genetic neural fuzzy system-based quality prediction model
for injection process. Computers and Chemical Engineering, 26,1253-1263.
Lin, T. R. (2003). Optimization technique for face milling stainless steel with multiple
performance characteristics. International Journal of Advanced Manufacturing
Technology, 19, 330-335.
McDonald, D. B., Grantham, W. J., Tabor, W. L., & Murphy, M. J. (2007). Global and local
optimization using radial basis function response models. Applied Mathematical
Modelling, 31, 2095-2110.
Metropolis, N. A., Rosenbluth, M., Rosenbluth, A., & Teller, E. T. (1953). Equation of state
calculations by fast computing machines. Journal of Chemical Physics, 21(6),
1087-1092.
Mok, S. L., & Kwong, C. K. (2002). Application of artificial neural network and fuzzy logic
in a case-based system for initial process parameter setting of injection molding.
Journal of Intelligent Manufacturing, 13(3), 165-176.
Mok, S. L., Kwong, C. K., & Lau, W. S. (2000). An intelligent hybrid system for initial
process parameter setting of injection moulding. International Journal of Production
Research, 38(17), 4565-4576.
Montgomery, D. (2009). Design and analysis of experiments. John Wiley & Sons, Inc.
Niu, B., Xue, B., Li, L., & Chai, Y (2009). Symbiotic multi-swarm PSO for portfolio
optimization. Lecture Notes in Computer Science, 5755, 776-784.
Ozcelik, B., & Erzurumlu, T. (2006). Comparison of the warpage optimization in the plastic
injection molding using ANOVA, neural network model and genetic algorithm. Journal
of Materials Processing Technology, 171(3), 437-445.
Seaman, C. M. (1994). Multiobjective optimization of a plastic injection molding process.
IEEE Transactions on Control Systems Technology, 2(3), 157-168.
Shi, F., Lou, Z. L., Lu, J. G., & Zhang, Y. Q. (2003). Optimization of plastic injection
molding process with soft computing. The International Journal Advanced
Manufacturing Technology, 21(9), 656-661.
Shi, Y., & Eberhart, R. C. (1998). A modified particle swarm optimizer. Proceedings of the
1998 IEEE International Conference on Evolutionary Computation, May 4-9,
Anchorage, Alaska.
Shie, J. R. (2008). Optimization of injection molding process for contour distortions of
polypropylene composite components by a radial basis neural network. International
Journal of Advanced Manufacturing Technology, 36,1091-1103.
Shiou, F. J., & Chen, C. H. (2003). Freeform surface finish of a plastic injection mold by
using ball-burnishing process. International Journal of Materials Processing
Technology, 140, 248-254.
Li, E., Jia, L., & Yu, J. (2002). A genetic neural fuzzy system-based quality prediction model
for injection process. Computers and Chemical Engineering, 26,1253-1263.
Lin, T. R. (2003). Optimization technique for face milling stainless steel with multiple
performance characteristics. International Journal of Advanced Manufacturing
Technology, 19, 330-335.
McDonald, D. B., Grantham, W. J., Tabor, W. L., & Murphy, M. J. (2007). Global and local
optimization using radial basis function response models. Applied Mathematical
Modelling, 31, 2095-2110.
Metropolis, N. A., Rosenbluth, M., Rosenbluth, A., & Teller, E. T. (1953). Equation of state
calculations by fast computing machines. Journal of Chemical Physics, 21(6),
1087-1092.
Mok, S. L., & Kwong, C. K. (2002). Application of artificial neural network and fuzzy logic
in a case-based system for initial process parameter setting of injection molding.
Journal of Intelligent Manufacturing, 13(3), 165-176.
Mok, S. L., Kwong, C. K., & Lau, W. S. (2000). An intelligent hybrid system for initial
process parameter setting of injection moulding. International Journal of Production
Research, 38(17), 4565-4576.
Montgomery, D. (2009). Design and analysis of experiments. John Wiley & Sons, Inc.
Niu, B., Xue, B., Li, L., & Chai, Y (2009). Symbiotic multi-swarm PSO for portfolio
optimization. Lecture Notes in Computer Science, 5755, 776-784.
Ozcelik, B., & Erzurumlu, T. (2006). Comparison of the warpage optimization in the plastic
injection molding using ANOVA, neural network model and genetic algorithm. Journal
of Materials Processing Technology, 171(3), 437-445.
Seaman, C. M. (1994). Multiobjective optimization of a plastic injection molding process.
IEEE Transactions on Control Systems Technology, 2(3), 157-168.
Shi, F., Lou, Z. L., Lu, J. G., & Zhang, Y. Q. (2003). Optimization of plastic injection
molding process with soft computing. The International Journal Advanced
Manufacturing Technology, 21(9), 656-661.
Shi, Y., & Eberhart, R. C. (1998). A modified particle swarm optimizer. Proceedings of the
1998 IEEE International Conference on Evolutionary Computation, May 4-9,
Anchorage, Alaska.
Shie, J. R. (2008). Optimization of injection molding process for contour distortions of
polypropylene composite components by a radial basis neural network. International
Journal of Advanced Manufacturing Technology, 36,1091-1103.
Shiou, F. J., & Chen, C. H. (2003). Freeform surface finish of a plastic injection mold by
using ball-burnishing process. International Journal of Materials Processing
Technology, 140, 248-254.
Su, C. T., & Chang, H. H. (2000). Optimization of parameter design: an intelligent approach
using neural network and simulated annealing. International Journal of Systems
Science, 31(12), 1543-1549.
Tay, K. M., & Butler, C. (1997). Modeling and optimizing of a MIG welding process-a case
study using experimental designs and neural networks. Quality and Reliability
Engineering International, 13(2), 61-70.
Tseng, H. Y. (2006). Welding parameters optimization for economic design using neural
approximation and genetic algorithm. The International Journal Advanced
Manufacturing Technology, 27(9), 897-901.
Tseng, W. J. (1998). Statistical analysis of process parameters influencing dimensional
control in ceramic injection molding. International Journal of Materials Processing
Technology, 79, 242-250.
Wang, Z., Gong, Z., Zhu, W., & Zhao, W. (2009). A rough set based PSO-BPNN model for
air pollution forecasting. 2009 Fifth International Conference on Natural Computation,
ICNC, 3, 357-361.
Wu, C. H., & Liang, W. J. (2005). Effects of geometry and injection-molding parameters on
weld-line strength. Polymer Engineering and Science, 45(7), 1021-1030.
Yang, Y. K., Shie, J. R., Yang, R. T., & Chang, H. A. (2006). Optimization of injection
molding process for contour distortion of polypropylene composite components via
design of experiments method. Journal of Reinforced Plastics & Composites, 25(15),
1585-1599.
Yang, Y., & Gao, F. (2006). Injection molding product weight: online prediction and control
based on a nonlinear principal component regression model. Polymer Engineering and
Science, 46(4), 540-548.
Yarlagadda, P. K. D. V. (2002). Development of an integrated neural network system for
prediction of process parameters in metal injection moulding. International Journal of
Materials Processing Technology, 130-131, 315-320.
Zhao, C., & Gao, F. (1999). Melt temperature profile prediction for thermoplastic injection
molding. Polymer Engineering and Science, 39(9), 1787-1801.

 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
QR Code
QRCODE

臺灣人文及社會科學引文索引資料庫

目前上線人數：652（臺灣：643，外國：9） ／ 本年度總使用人次：2393369 ／ 訪客人次（自102年09月）：68188737
國家圖書館著作權聲明 Copyright © 2013 All rights reserved.
本館地址: 100201臺北市中山南路20號． 本館地圖及交通資訊．
總機：(02)23619132．最佳瀏覽解析度為1024x768以上